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Superalgos Algorithmic Trading

Superalgos Algorithmic Trading solves all major trading infrastructure concerns so that you may focus solely on what truly matters: evolving trading intelligence.

To such extent is infrastructure taken care of, that—truth be told—both simple and complex trading strategies may be built without coding. However, because you can code, you should be able to achieve things non-coders may not.

Your first and foremost resource is the option of extending the trading system capabilities by using JavaScript code instead of the simpler mathematical statements used by non-coders, to define trading rules and formulas.

The second major resource is data mines. Superalgos Algorithmic Trading sets you up to take all market data available, process it by performing calculations and studies, and storing new datasets that strategies may consume.

A Starter's Guide to Coding Killer Crypto-Trading Bots

Learn how Superalgos Algorithmic Trading solves all major trading infrastructure requirements including exchange connectivity, trading engine, access to reliable data, strategy testing environment, and the implementation of a trading framework paired with the Superalgos Trading Protocol so that developers may focus on what truly matters: evolving trading intelligence.

Building, Testing and Deploying Trading Strategies

The Superalgos Suite offers a consistent and integrated workflow to build, test and deploy trading systems, with the Superalgos Trading Protocol as the guiding thread.

To build a strategy, you will program the rules to call the strategy's trigger on and trigger off events, as well as the take position event. You will define formulas for the position size and will set the initial stop and initial take profit targets. Those targets may be dynamically managed in phases triggered when certain market situations arise. A trading system manages any number of strategies within a specific market, and you may have as many trading systems as you wish.

The platform features a trading bot whose job is to evaluate the data made available by sensor and indicator bots applying the trading logic defined on trading systems. As a result, the trading bot produces, on one side, a complete trading simulation outputting datasets that include trades, the action of strategies, validation or rules, etc. On the other side, the trading bot manages the execution of orders when running on forward-testing and live trading sessions.

The simulations stored in datasets are rendered over the charts by plotters, allowing the user to see strategies in action in real-time, trade by trade, directly over the charts. Such features are instrumental in the process of fine-tuning strategies, as you may analyze—on a trade by trade basis—what is going well and what may be improved. Tuning a strategy is usually an iterative process going back and forth between the strategy rules and the results over the charts.

Touch up a rule, run a quick backtesting session, analyze the results on the charts, and go back to tweaking the rules. Spend some time optimizing, and you will hopelessly fall for a thrilling game in the quest to push ROI higher with each tweak!

Data Mines

A data mine is a unique concept by which developers may leverage raw market data and indicators to produce more elaborate studies, and make them accessible to trading strategies, creating significant competitive advantages.

The platform's focus is on resolving infrastructure requirements to help developers increase productivity. In the case of data mines, the system provides a visual interface on which users define the workings of processes and data products without any coding requirements.

Processes use data produced by sensors and indicators as input. After performing any number of custom calculations, they produce custom data products as outputs. Users define status and data dependencies, and the architecture of the output datasets on the visual interface.

The one thing you will need to code is the calculations procedure. Everything else, starting with administering dependencies with other processes to producing standardized datasets for each time frame (from 1 minute to 24 hours intervals), is handled internally by the system.

The algorithmic trading features described in this page are built-in the Superalgos software. No matter what focus you start with, you will always be able to tap into all the features and functionality in the suite, including those associated with other fields.

Getting Started

Follow the instructions in the documentation to get up and running with the Superalgos Suite. You'll find it useful throughout the learning curve.

Should you need any help, please direct all questions to the Superalgos Telegram Group. The Community will be happy to help you.

Make sure you take a look at the overall features of the Superalgos Crypto Intelligence Suite. The intro video will quickly get you up to speed.

The Future of Algorithmic Trading

Superalgos Algorithmic Trading strives to become the most advanced, flexible, reliable and comprehensive platform to build, test and deploy trading bots. While the basic infrastructure is robust enough, the platform needs to keep evolving and grow in several directions.

The next milestone is providing robust control over execution. Users should be able to use limit orders, monitor the filling of orders, move orders when not filled, fragment orders, set multiple stop-loss and take profit targets, set and manage execution ranges, and so on.

Another significant milestone will be the ability to access multiple markets and even multiple exchanges from within the same strategy. Such features will unleash interesting possibilities at the strategy level, including the option of spreading orders across multiple exchanges.